SENTIMENT AND BUSINESS ANALYSIS OF RUSSIA-UKRANE WAR
BY
IMONIKHE IRIA AYENI
20229033
M.Sc Data Science
CIS7029
SUBMITTED
TO
SOCIAL MEDIA ANALYTICS FOR BUSINESS (CIS7029)
CARDIFF METROPOLITAN UNIVERSITY
LLANDAFF CAMPUS
MAY, 2023
Table of Contents
ABSTRACT ................................................................................................................................................ 4
Purpose ............................................................................................................................................... 4
Design .................................................................................................................................................. 4
Findings ............................................................................................................................................... 4
Business implications .......................................................................................................................... 4
Research limitations ............................................................................................................................ 4
BIOGRAPHY ............................................................................................................................................. 5
INTRODUCTION ....................................................................................................................................... 5
Background to the Study .................................................................................................................... 5
RESEARCH DESIGN .................................................................................................................................. 6
Aims and Objectives ............................................................................................................................ 6
Research Questions ............................................................................................................................ 6
Research methodology ....................................................................................................................... 7
DISCUSSION OF DATA SOURCES / TOOLS/ APIS AND JUSTIFICATION OF CHOICE .................................. 8
Data Sources ....................................................................................................................................... 8
Facebook: ........................................................................................................................................ 8
Twitter: ............................................................................................................................................ 8
Reddit: ............................................................................................................................................. 8
Kaggle: ............................................................................................................................................. 8
Data Scraping Tools and APIs .............................................................................................................. 9
Twitter API ...................................................................................................................................... 9
Tweepy ............................................................................................................................................ 9
SNscrape ......................................................................................................................................... 9
Twint ............................................................................................................................................... 9
Sentiment Analysis Tools .................................................................................................................... 9
Vader ............................................................................................................................................... 9
Textblob ........................................................................................................................................ 10
NLTK .............................................................................................................................................. 10
Data Visualising Tools ....................................................................................................................... 10
Tableau .......................................................................................................................................... 10
Power BI ........................................................................................................................................ 10
Python Visualisation Library .......................................................................................................... 10
Justification of Choice ................................................................................................................... 11
VISUALIZATION OF KEY RESULTS AND STORY TELLING ........................................................................ 11
Sentiment Analysis: CNN and Al Jazeera Twitter Followers ............................................................. 11
Sentiment Analysis: General Twitter Users ...................................................................................... 18
Sentiment Analysis of Reddit Users ...................................................................................................... 23
ECONOMIC IMPLICATIONs OF THE WAR .......................................................................................... 26
CONCLUSIONS, PROJECT LIMITATIONS AND RECOMMENDATIONS .................................................... 29
Conclusions ....................................................................................................................................... 29
Limitations ........................................................................................................................................ 30
Recommendations ............................................................................................................................ 30
APPENDICE ............................................................................................................................................ 32
ABSTRACT
This paper presents sentiment and business analysis of the Russia-Ukraine War.
Purpose
This research work is designed to explain the public's polarity towards the war in Ukraine. It
determines the point of convergence of CNN and Al Jazeera Twitter followers on the war in
Ukraine. It presents the business implications of the war for the economies of Ukraine and
Russia.
Design
This research paper relied on primary and secondary sources of data. It employed random
and stratified sampling methods to source and analyse data from Twitter and Reddit using
SNScrape and Twint. Kaggle Open Data Source was my secondary source of data; this was
used to support the business implications of the war.
Findings
The data obtained was cleaned, rigorously analysed, and visualised with Python and
Tableau. It shows that Al Jazeera has better coverage of the war than CNN. There was a
general agreement that the Russia-Ukraine war is negative in all its ramifications.
Business implications
The war has greatly impacted the economy of Ukraine, affecting the general business life
cycle. Russia has suffered enormous losses from the war in personnel and equipment. The
paper captures the need to immediately end the war.
Research limitations
This study was limited by its inability to scrape data directly from CNN and Al Jazeera sites.
BIOGRAPHY
My name is Imonikhe Iria Ayeni. I have bachelor's and master's degrees in geography and
am currently undertaking a master’s degree in data science. I have strong expertise in
Python, SQL, and tableau, with a focus on geospatial data analytics and social media data
analytics for business.
I have years of experience in the research and development department of one of the
biggest banks in Nigeria. Our duty is to manage the bank's brand, manage the life-cycle of
the bank's products and services, conduct geospatial surveys for the utmost site to establish
branches, and advise the bank's board of management. We often analyse government
policies that could affect the growth of our business.
INTRODUCTION
Background to the Study
Global businesses were just beginning to recover from the dwindling profits that followed
the coronavirus pandemic when missiles began raining on Ukraine. On February 22, 2022,
the news of Russia’s invasion of Ukraine made headlines on virtually all telecommunication
media. So far, the effects of the war have had a far-reaching effect on global economies and
tremendously impacted local and international businesses. From an immediate angle, the
economics of Ukraine have greatly nosedived. International and local businesses in Ukraine
have closed shop (refer to Appendix 1 and 2), and their physical structures have been
obliterated by bombs and grenades. Russia, the country that threw the first bomb, has also
had its fair share of the impact of the war on its economy and businesses within its territory.
The countries bordering Russia and Ukraine and countries in faraway Africa and Oceania
have felt the bite of the war in agriculture, oil and gas, stock market prices, and many other
sectors (Towey, et al, 2022; Beaubien, 2022)
People have different opinions about a war, and to some, the war is positive, as they argue
it enables them to gain economic and political power. "Political and economic power grows
out of the barrel of a gun, whoever has power has everything." This phrase is credited to
Chairman Mao. The phrase was used by Mao during an emergency meeting of the Chinese
Communist Party (CCP) on August 7, 1927, at the beginning of the Chinese Civil War (Li
Gucheng, 1995).
From history, the world has had and has many war-lovers. From the early religious crusaders
who gleefully wiped out a tribe, race, sect, or people to modern world leaders who invade
other countries for "seemingly" political and economic gains (Bigelow, 1969; Van der
Dennen, 1995).
Popular American warmongers are Theodore Roosevelt, Thomas Brackett Reed, Henry
Cabot Lodge, William James, and William Randolph Hearst (Steel Ronald, 2010). Japan and
Israel will never forget the impact of the Adolf Hitler War on their environment and
economies.
It is obvious that the world is polarised between warmongers (war-lovers) and pacifists
(war-haters). Social media is one place where people express their feelings, emotions, and
sentiments. People use social media to keep up with world events, news, wars, and natural
disasters and to share their opinions via likes, comments, and shares, among other things.
Today’s most popular social media platforms are Facebook, Twitter, Instagram, YouTube,
and Reddit. (Salam and Gupta, 2018)
Some world leaders are talking tough and making comments that can further escalate the
war and plunge the global economy into a recession that we will not recover from in
centuries, and worst of all, it will plunge us into a third world war. This paper presents public
opinion of the war using machine learning and artificial intelligence models. It critically
analyses the economic implications of the war for Russia and businesses in Russia, the
supposed initiator of the war. The paper assumes that Ukraine has been plundered
economically. It is my belief that this paper would serve as a policy check for world leaders
who believe in war as a means of achieving political and economic power.
RESEARCH DESIGN
The design of the research is built around its aims and objectives.
Aims and Objectives
The aim of this research investigation is to analyse the sentiments and examine the business
and economic implications of the Russia-Ukraine war. The following are the specific
objectives:
1. To determine the best tools to scrape data and analyse public perceptions.
2. To explain the public's polarity towards the war in Ukraine.
3. To determine the degree of divergence and convergence of Twitter and Reddit users
towards the war in Ukraine.
4. To determine the point of divergence and convergence of tweets on CNN and Al
Jazeera's Twitter handles on the war in Ukraine
5. To examine the business implications of the war on the economy of Russia and
Ukraine.
Research Questions
The study will attempt to provide answers to the following research questions.
1. What are the best tools to scrape data and analyse public opinions?
2. How is the public polarised towards the war in Ukraine?
3. What is the degree of divergence and convergence among Twitter and Reddit users
towards the war in Ukraine?
4. What is the point of divergence and convergence of tweets on CNN and Al Jazeera's
Twitter handles on the war in Ukraine?
5. What are the business implications of the war on the economy of Russia and
Ukraine?
Research methodology
This study used Twint and SNScrape to scrape data from Twitter and Reddit, and it was
supported by the Kaggle data set.
I scraped about 11,000 tweets from Twitter and 11,000 comments from Reddit. I scraped
data from both social media platforms using SNScrape and Twint. I had planned to use
Scrapy, but I could not get a Twitter developer licence. I eventually settled on Social
Networking Service (SNS) scrape and Twint, because they gave me the ability to manipulate
my search parameters, and with a few lines of code, I was able to get search results in a few
minutes.
I scrapped data from January 2022 to April 2023, however, some dates were dropped in the
process of data cleaning as they came out as duplicates. Data scraping and analysis were
done from specific to general. I first zeroed in on the sentiment analysis of Al Jazeera and
CNN followers on Twitter. This was to satisfy my assumption that these news media
followers are better informed and will better tweet with more information. I used Twint to
scrape data in this category. This also enabled us to ascertain the degree of sentiment
convergence on the war between western and eastern news media and their followers.
On the general angle, data scraping was done on six-time divisions with Twitter, targeting
general Twitter users. This is to avoid skewing my data in one or two months. I imagined
that I could get as many as 100,000 tweets in one month if this was not done. This time
division also enabled me to see the trend in polarity over time. The search parameters are
Russia Ukraine war, war in Ukraine, Russia invade Ukraine, Ukraine war
The scrapped data was then subjected to machine learning and artificial intelligence tools to
clean the data and extract useful meaning. The tools employed in this case were NLTK,
Textblob, and Vader sentiment analyzers.
To ascertain the economic implications of the war, I used a Kaggle data source.
DISCUSSION OF DATA SOURCES / TOOLS/ APIS AND JUSTIFICATION OF CHOICE
Data Sources
This section discusses research data sources.
Facebook:
Facebook is regarded as the most popular social networking platform where users can post
comments, share photographs, post links to news or other interesting content on the web,
chat live, and watch short-form videos. Facebook started in February 2004 as a school-based
social network at Harvard University. It was created by Mark Zuckerberg and Edward
Saverin, both were students at Harvard (Nations, 2021).
Twitter:
One of the most widely used social networking sites worldwide is Twitter. A whopping 396
million people use Twitter, producing and consuming content on a vast scale. Twitter is a
platform where people use brief messages, or tweets, to share their thoughts, feelings, and
opinions whenever they want. In those brief messages, broader communities—such as the
perspectives of people in a particular country—as well as individuals' emotional states of
mind—such as happiness, worry, and hopelessness—are overtly or covertly captured
(Hassan, et al., 2014).
Reddit:
Reddit is a social media platform that was founded on June 23, 2005, by Steve Huffman and
Alexis Ohanian in Medford, Massachusetts. Since its inauguration in 2005, Reddit has been a
viable platform to share and discuss topics and ideas and upvote them accordingly. The
forum is a place to learn, share, debate, and build community. Reddit is one of the most
visited websites in the world and is one of the largest forums to exist (Marsh, 2021).
Reddit had over 300 million posts in 2020. As at 2021, Reddit had over 2.6 million
subreddits. A subreddit is a thread created by users that focuses more on a specific topic
(Marsh, 2021).
Kaggle:
Kaggle is a free database platform and a cloud-based workbench for data science, and
artificial intelligence education. It was launched in 2010. Its top managers were Anthony
Goldbloom and Jeremy Howard. It was bought by Google in 2017 (Nemhe, 2022).
Data Scraping Tools and APIs
Twitter API
Twitter API is an endpoint that can be used to scrape and analyse Twitter data, It requires a
Twitter developer account to use. (Zoltan, 2022).
Tweepy
Tweepy is a great library built upon the Twitter API, which allows for easy access to data and
to perform complex tasks. It allows researchers and developers to take full advantage of all
Twitter API features and is a widely used tool (Zoltan, 2022).
SNscrape
Snscrape is a scraper for social networking services (SNS). It scrapes things like user profiles,
hashtags, or searches and returns the discovered items, e.g., the relevant posts. Snscrape
doesn’t go through the Twitter API and it can scrape other social media platforms, such as
Reddit, Youtube (Zoltan, 2022).
Twint
Twint is a complete Twitter scraping tool able to scrape tweets from specific users, topics,
hashtags, locations, and more without the need to connect to the Twitter API. (Zoltan,
2022).
Sentiment Analysis Tools
Sentiment analysis is a text analysis technique that finds polarity in a text. It could be an
entire document, a paragraph, a sentence, or a clause. Sentiment analysis seeks to quantify
a speaker's or writer's attitude, sentiments, assessments, attitudes, and emotions based on
the computer's handling of subjectivity in a text (Beri, 2020).
Vader
A vocabulary and rule-based sentiment analysis tool called VADER (Valence Aware
Dictionary and Sentiment Reasoner) is customised precisely to the sentiments expressed in
social media (Singh, 2020; Beri, 2020).
The text sentiment is calculated using a collection of lexical features (such as words) that are
classified as positive or negative based on their semantic orientation. The likelihood that a
sentence will be good, negative, or neutral is provided by the Vader sentiment function.
(Afaf, 2021)
Textblob
TextBlob is a Python library for Natural Language Processing (NLP). Textblob uses the
Natural Language Toolkit (NLTK) to analyse sentiments. TextBlob returns a sentence's
polarity and subjectivity. The range of polarity is [-1, 1]. Additionally, subjectivity is a float
with a value between [0,1]. (Afaf, 2021).
NLTK
The Natural Language Toolkit (NLTK) is a Python library for working with human language
data. NLTK has many tools and resources for tasks such as tokenization, part-of-speech
tagging, and stemming, amongst others (Otten, 2022 )
Data Visualising Tools
Tableau
Tableau was founded in 2003 as a result of a computer science project at Stanford that
aimed to improve the flow of analysis and make data more accessible to people through
visualisation.
Tableau offers an expansive visual business intelligence and analytics platform, and is widely
regarded as the major player in the marketplace. The company’s analytic software portfolio
is available through three main channels: Tableau Desktop, Tableau Server, and Tableau
Online, (Tim, 2022).
Power BI
This is a Microsoft is a major player in enterprise BI and analytics. Power BI is cloud-based
and delivered on the Azure Cloud (Tim, 2022).
Python Visualisation Library
Matplotlib
The most well-known and widely-used charting library in the Python community is called
Matplotlib, a data visualisation and 2-D plotting toolkit. It was first released in 2003.
Seaborn
Seaborn is a Python data visualisation library that is based on Matplotlib and closely
integrated with NumPy and Pandas data structures. Other Python visualisation tools are
plotly and GGplot, among others.
Justification of Choice
Among the various data sources, I chose Twitter, Reddit and Kaggle. Twitter and Reddit
allow crawling and data scraping. Kaggle is an open data source, and it is known to be a
workbench for data science.
Amongst the various data scraping tools, I chose Twint and SNScrape as they do not require
a developer licence, and are fast and easy to use.
Among the various sentiment analytics tools, I chose the Python packages Vader, Textblob,
and NLTK. This is largely because they are easy and have a large and open community.
Amongst the various visualisation tools, I chose Tableau and Python as they are easy to use,
easy to manipulate, and produce top-notch visualisations.
VISUALIZATION OF KEY RESULTS AND STORY TELLING
This section is divided into four sections, starting from the specific to the general. It starts
with discussing the sentiment polarity of CNN and Al Jazeera followers on Twitter. It
progresses to the general Twitter and Reddit users, and the section concludes with the
economic and business implications of the war. The visualisation was done with Python and
Tableau.
Sentiment Analysis: CNN and Al Jazeera Twitter Followers
This section presents key visualisations and discussions of sentiment among Twitter
followers of CNN and Al Jazeera. The data scraping tool is Twint, and visualization was done
with Python and Tableau.
Fig 1: Sentiment count by News Media
Fig. 1 shows that Aljazeera followers expressed greater sentiments about the war, with 622,
and CNN had 334. This difference in the number could be a result of many factors, but the
research noticed that Al Jazeera had better coverage of the war. A review of the official
websites of both international media houses showed that Al Jazeera had better statistics
about the war. Attempt to confirm this position was not successful, as I could not scrape the
websites of media houses, crawling and data scraping are disallowed.
Fig 2: Polarity Count
Fig. 2 shows that our polarity was divided between negative and positive sentiments, and no
one expressed a neutral sentiment. 95.19% of the sampled population expressed a negative
sentiment about the war, as shown in Fig. 3.
Fig 3: Percentage of Respondents
Fig 4: Sentiment Polarity by News Media
Fig. 4 explains the sentiments expressed by the news media, with Al Jazeera followers
having a 595 count of negative sentiments and 27 counts of positive comments. CNN had
315 and 19 negative and positive sentiments, respectively.
Fig 5: Retweet Count by Polarity
Fig. 5 shows that 86,920 people who probably had no time to comment expressed their
dissatisfaction with the war by way of retweeting the negative comments. This is about
1884% of the number of people who retweet a positive comment.
Fig 6: Likes Count by Polarity
Fig. 6 shows that 323,511 of the sampled group liked negative sentiments. This further
explains how unpopular the war is.
Fig 7: Likes Polarity by News Media
Fig. 7 shows that both news media's Twitter followers have huge likes of negative
sentiments about the war. Al Jazeera had 152,828 and CNN had 170,683 likes of negative
comments, while likes of positive comments were 5,497 and 19,172 for Al Jazeera and CNN,
respectively.
The study therefore shows that there is a convergence of negative sentiments about the
Russia-Ukraine war between Aljazera and CNN, the followers of both media houses on
Twitter expressed overwhelming negative sentiments about the war. Though Aljazeera had
more of its followers commenting about the war, CNN has a higher following, as reflected in
the number of likes and retweets.
It is obvious so far that people who had no time to formally make a comment, expressed
their sentiments by way of likes and retweets. With negative sentiments receiving a huge
number of likes and retweets.
The word cloud shows very salient words and their frequency of usage; a few amongst them
are war crimes, children, killed, United States, and Joe Biden.
Figure 8: World Cloud and Bag of Words
The word-cloud and bar chart show the frequency of words. It shows that not just the
business infrastructure has been destroyed in Ukraine; the children who are supposed to
carry on the business life circle have also been displaced or even killed. The word cloud also
shows that Russia can be charged with war crimes. The word cloud also shows the impact
of Joe Biden, China, and Poland on the war. It shows a clamour to end the war
Sentiment Analysis: General Twitter Users
This section presents key visualisations of results from general Twitter users. The scraping
tool is SNscrape, and visualisation was done with Python and Tableau.
Fig 9: Code snippet and percentage of polarity
The study showed that 89% of Twitter users expressed negative sentiments about the war in
Ukraine. 10.4% expressed a positive comment, and only 0.55% were neutral.
Fig 10: Pie showing percentage of Polarity
Fig 11: Trend count by Date Month
Fig 12: Trend of Count of Sentiment for Date Day
Figs. 11 and 12 show that as the war continued, there was a very sharp increase in negative
sentiments. The time trend also presupposed that people were more informed about the
cause, business and economic implications, and general atrocities that accompanied the
war, and they expressed their strong dissatisfaction with the war.
Fig 13: Twitter likes Count
Fig. 13 shows that 102,680 people who probably had no time to comment expressed their
dissatisfaction with the war by liking the negative comments. This is about 131% of the
number of people who like a positive comment
Fig 14: Twitter Retweet Count
Source: Scraped Twitter Tweets (2023)
Fig 14 shows that re-tweet of negative comment is more than a 100% of the retweet of
positive comments. This again explain the high negative sentiment about the Russia Ukraine
war
Fig. 15: Bag of Most Frequently Used Words by Bar Chart
Fig 16: Tweet Word Cloud
The word cloud from general Twitter users captures the business and economic implications
of the war with words like inflation and price. It is expected that there will be a scarcity of
food and goods, and as businesses close shops, the price of commodities is bound to
increase. There is a general clamour to end and stop the war.
Sentiment Analysis of Reddit Users
This section presents key visualisations of results from Reddit subscribers. The scraping tool
is SNscrabe, and visualisation was done with Python and Tableau.
Fig 17: REDDIT SENTIMENT POLARITY
Fig. 17 shows that 6672 Reddit subscribers expressed negative sentiments about the war,
3,080 expressed positive sentiments about the war, and 1351 subscribers were neutral.
Fig 17 shows that 60% of the sampled Reddit users expressed negative sentiments about the
war.
Fig 18: Bag of Words and Word Cloud
Salient words from the word cloud and word frequency are world war, war crimes, and end
war. If this war is not ended, it could result in the Third World War. The word cloud again
emphasised the impact and position of the West and the United States on the war. To some
Reddit users, the West and America (NATO) are the reason for the war.
ECONOMIC IMPLICATIONs OF THE WAR
This section briefly discusses the economic and business implications of the Russia-Ukraine
war. It has already been established in the previous sections that the general business
infrastructure has been destroyed. The children that are supposed to continue the business
life circle have been displaced, rendered refugees, or killed.
Fig 19: Russia's Equipment Losses by Date
Source: Ivaniuk (2022)
Source: Ivaniuk (2022)
Fig 20a: Russia Equipment Losses by Quarter Date
Source: Ivaniuk (2022)
Fig 20b: Russia Equipment Losses by Quarter of Date
Source: Ivaniuk (2022)
Shows Russia's equipment lost to the war. It explains that Russia, the supposed initiator of
the war, is feeling the sting of the war
Fig 21: Russia’s Personnel Losses
Source: Ivaniuk (2022)
Shows the number of personnel Russia has lost to the war. The figure stands at 5,462,040 as
of April 2023.
CONCLUSIONS, PROJECT LIMITATIONS AND RECOMMENDATIONS
Conclusions
This study analysed the sentiment and business implications of the war in Ukraine. The
study successfully used Twint and SNscrape to scrape data from two major social media
platforms: Twitter and Reddit. It scraped data from specific to general, that is, Twitter
followers of CNN and Al Jazeera, then general users of Twitter, and general users of Reddit.
The study noticed that Al Jazeera has better coverage of the war, while CNN has more
followers on Twitter. The study noticed that both news media followers expressed negative
sentiments about the war. The study noticed some salient words, including children, killed,
and war crimes. This means that children have been displaced and killed as a result of the
war, and there is enough evidence to sue Vladimir Putin for war crimes.
At a general angle, 89% of Twitter users expressed negative sentiments about the war, and
69.09% of Reddit users expressed negative sentiments about the war. Twitter's users
effectively captured the business angle and economic angle of the war, they showed that
the war could permanently distort the business life circle as children are being hit by the
war.
The Kaggle dataset paints a picture of losses to Russia as a result of the war, capturing the
figure of personnel losses at 5.4 million and huge equipment losses. There is a drag of the
West, the United States, and the NATO confederation in the war in Ukraine. There is a
general agreement from every angle, perspective, or sentiment that the war needs to end
immediately to avoid a third world war.
Limitations
The study was limited by the inability to scrape CNN and Al Jazeera sites. I would have been
to show more facts and statistics about the war, and be able to better ascertain any bias
between the media houses
Recommendations
It is wrong to think that economic and political progress comes through the barrel of a gun.
Rather, a war has a catastrophic effect nowadays, on international business, the global
economy, life, and the peace of the warring nations. It is my recommendation that the
warring nations resume peace negotiations and end the war.
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APPENDICE
This section presents video chat link, tableau public links, Google Colab link, code snippet, business
affected and general pictures from the war. Some pictures show the strength of the human spirit in
the face of devastation and disaster
LINK TO TEAMS CHAT WITH TUTOR
https://outlookuwicac-
my.sharepoint.com/:v:/g/personal/st20229033_outlook_cardiffmet_ac_uk/EZZ9lR8XvZRHr3Wp7Pbs
sZ8BeuVHi7LilURpH-Uzs_mouw
LINK TO TABLEAU PUBLIC VISUALISATION
CNN AND AL JAZEERA SENTIMENT VISUALISATION
https://public.tableau.com/authoring/IMONIKHEAYENI/Dashboard1#1
LINK TO COLAB
TWINT WITH TWITTER FOLLOWERS OF CNN AND ALJAZEERA
https://colab.research.google.com/drive/1CLptIbR2YQwsZI3kzbWE_mrPlXkHJl_M
SNSCRAPE WITH GENERAL REDDIT USERS
https://colab.research.google.com/drive/1pjhXwAa9wIBXZ0DSQPwc0wUT7JhnVTks
Scrape your data
DATA SCRAPING TWINT
Packages
